Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably – locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: on average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations on the order of 10 ± 24 % of the mean ice thickness (1σ estimate). Models relying on multiple data sets – such as surface ice velocity fields, surface mass balance, or rates of ice thickness change – showed high sensitivity to input data quality. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches.

Estimation of glacier ice-thickness distribution is important for many glacio-hydrological applications such as runoff projections, glacial lake outburst flood (GLOF) predictions, future evolution of glaciers. Varieties of modelling approaches are available for estimating ice-thickness distribution depending upon the data availability. In the present study, we estimated the ice-thickness distribution and total ice volume of Chhota Shigri Glacier using an optimally parameterized Glacier Bed Topography version 2 (GlabTop2) model, a shallow ice approximation (SIA)-based spatially distributed approach. Among the model input parameters, the shape factor (f), a non-measurable factor compensating for unaccounted effects such as valley shape needs to be calibrated using field measurements of ice-thickness. However, the lack of direct measurements of ice-thickness over many glaciers worldwide restricts model calibration and effective implementation. Therefore, to overcome this limitation, in this study, a novel approach using a relationship between shape factor, glacier cross-sectional width, and ice-thickness at the centre of a cross-section has been proposed and also tested to estimate optimal shape factor of the study glacier. Additionally, a detailed analysis of the effect of Digital Elevation Model (DEM) resolution and shape factor parameterization on the modelled ice-thickness estimates indicate that improving either the DEM resolution or calibrating the shape factor individually will not lead to improved ice-thickness estimates. In fact, both are necessary for better estimation of ice-thickness distribution. The high resolution DEM used in this study is TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) DEM of 10 × 10 m grid size. Finally, a comparison of the results from a previous study where they used Shuttle Radar Topography Mission (SRTM) 90 m DEM indicates that the improved parameterisation of GlabTop2 model has led to a reduction in the error bounds of the estimated ice-thickness including the total glacier stored ice volume for the year 2013, which is estimated to be 1.74 ± 0.25 km³. Furthermore, based on the obtained results, it can be said that the GlabTop2 model combined with the proposed parameterization approach is having enormous potential to be applied over the wide range of data scarce Himalayan glaciers to quantify reliable ice-thickness estimates.

This study has been done using Polarimetric Synthetic Aperture Radar (SAR) data to estimate the snow physical properties (snow wetness and snow density), inManali sub-basin of Himachal Pradesh, India. The SAR data used are of Radarsat-2 (RS2) and Environmental Satellite, Advanced Synthetic Aperture Radar (ASAR). SAR-based inversion models were implemented in Mathematica and MATLAB, and have been used for finding out wet and dry snow dielectric constant, snow wetness and snow density. The SAR-based inversion models were implemented separately for fully polarimetric RS2 and dual polarimetric ASAR Alternate Polarization System datasets. Masks for forest, built area, layover and shadow were considered in estimating snow parameters. The overall accuracy in terms of R ² value comes out to be 0.86 for snow wetness and 0.84–0.72 for snow density based on the ground truth data for subset area of Manali sub-basin of Beas river up to Manali.